Papers by Alex Wang
GRASP: A Disagreement Analysis Framework to Assess Group Associations in Perspectives (2024.naacl-long)
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Vinodkumar Prabhakaran, Christopher Homan, Lora Aroyo, Aida Mostafazadeh Davani, Alicia Parrish, Alex Taylor, Mark Diaz, Ding Wang, Gregory Serapio-García
| Challenge: | Recent work shows that ignoring rater subjectivity is problematic within specific tasks and for specific subgroups. |
| Approach: | They propose a disagreement analysis framework to measure group association in perspectives among different rater subgroups. |
| Outcome: | The proposed framework reveals specific rater groups that have significantly different perspectives than others on certain tasks and helps identify demographic axes that are crucial to consider in specific task contexts. |
Label Representations in Modeling Classification as Text Generation (2020.aacl-srw)
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| Challenge: | Existing methods for text generation use strings to represent labels . linguistic properties of labels do affect performance, though their results are limited to document retrieval. |
| Approach: | They investigate the effect of string representations on how effectively a model learns a task . they use four standard text classification tasks to model string representation . |
| Outcome: | The proposed model improves on four standard text classification tasks . the results are largely negative in the low data setting . |
QuestEval: Summarization Asks for Fact-based Evaluation (2021.emnlp-main)
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Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano, Alex Wang, Patrick Gallinari
| Challenge: | Existing evaluation metrics for summarization evaluation are limited and do not correlate well with human judgments. |
| Approach: | They propose to extend existing evaluation metrics to include question answering models to assess whether a summary contains all relevant information in its source document. |
| Outcome: | The proposed framework significantly improves the correlation with human judgments over four evaluation dimensions. |
Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models (2024.acl-long)
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Jinhao Duan, Hao Cheng, Shiqi Wang, Alex Zavalny, Chenan Wang, Renjing Xu, Bhavya Kailkhura, Kaidi Xu
| Challenge: | Large Language Models (LLMs) show promising results in language generation but often “hallucinate”, making their outputs less reliable. |
| Approach: | They propose to shift attention to more relevant components at token- and sentence-levels for better UQ. |
| Outcome: | The proposed approach improves the performance of a range of popular “off-the-shelf” LLMs with model sizes extending up to 33B parameters. |
Let’s Think Frame by Frame with VIP: A Video Infilling and Prediction Dataset for Evaluating Video Chain-of-Thought (2023.emnlp-main)
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Vaishnavi Himakunthala, Andy Ouyang, Daniel Rose, Ryan He, Alex Mei, Yujie Lu, Chinmay Sonar, Michael Saxon, William Wang
| Challenge: | Existing studies show vision-language systems can reason about images using natural language, but their capacity for video reasoning remains underexplored. |
| Approach: | They propose to frame video reasoning as the sequential understanding of a small number of keyframes, thereby leveraging the power and robustness of vision-language systems' capacity to reason about images using natural language. |
| Outcome: | The proposed models can generate multiple intermediate keyframes and predict future keyframe, and they perform poorly on GPT-4, GPT-3, and VICUNA. |
Self-Ensemble: Mitigating Confidence Distortion for Large Language Models (2025.findings-emnlp)
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| Challenge: | Large Language Models exhibit a confidence distortion problem on multichoice question-answering . Self-Ensemble solves this problem by splitting the choices into several groups . |
| Approach: | They propose a method that splits LLM choices into several groups and ensembles them to reach a final decision. |
| Outcome: | The proposed method outperforms standard inference and baseline methods on MCQA. |
BLiMP: The Benchmark of Linguistic Minimal Pairs for English (2020.tacl-1)
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Alex Warstadt, Alicia Parrish, Haokun Liu, Anhad Mohananey, Wei Peng, Sheng-Fu Wang, Samuel R. Bowman
| Challenge: | Recent studies have examined how linguistic knowledge of language models (LMs) varies across English phenomena. |
| Approach: | They propose a benchmark to evaluate linguistic knowledge of language models on major grammatical phenomena in English. |
| Outcome: | The proposed benchmark evaluates the linguistic knowledge of language models on major grammatical phenomena in English. |
On Measuring Social Biases in Sentence Encoders (N19-1)
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| Challenge: | Word embeddings such as word2vec and GloVe exhibit human-like implicit biases based on gender, race, and other social constructs. |
| Approach: | They propose a simple generaliza test to measure bias in word embeddings by comparing two sets of target-concept words to two sets . |
| Outcome: | The proposed test shows that word2vec and word2Ve exhibit human-like implicit biases based on gender, race, and other social constructs. |
What Do NLP Researchers Believe? Results of the NLP Community Metasurvey (2023.acl-long)
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Julian Michael, Ari Holtzman, Alicia Parrish, Aaron Mueller, Alex Wang, Angelica Chen, Divyam Madaan, Nikita Nangia, Richard Yuanzhe Pang, Jason Phang, Samuel R. Bowman
| Challenge: | Getting sociological beliefs wrong can slow research and lead to wasted effort, missed opportunities, and needless fights. |
| Approach: | They present the results of the NLP Community Metasurvey, run from May to June 2022. |
| Outcome: | The NLP community metasurvey elicited opinions on controversial issues from May to June 2022. |
ASSERT: Automated Safety Scenario Red Teaming for Evaluating the Robustness of Large Language Models (2023.findings-emnlp)
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| Challenge: | Existing models do not provide robustness evaluations for large language models, but we find that they are inconsistent in performance. |
| Approach: | They propose to use semantically aligned augmentation, target bootstrapping, and adversarial knowledge injection to generate a test suite of prompts covering diverse robustness settings. |
| Outcome: | The proposed system generates a set of prompts covering diverse settings covering semantic equivalence, related scenarios, and adversarial. |
Investigating BERT’s Knowledge of Language: Five Analysis Methods with NPIs (D19-1)
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Alex Warstadt, Yu Cao, Ioana Grosu, Wei Peng, Hagen Blix, Yining Nie, Anna Alsop, Shikha Bordia, Haokun Liu, Alicia Parrish, Sheng-Fu Wang, Jason Phang, Anhad Mohananey, Phu Mon Htut, Paloma Jeretic, Samuel R. Bowman
| Challenge: | Recent work evaluating sentence representation models' knowledge of grammar has been slower to emerge. |
| Approach: | They propose five experimental methods inspired by prior work evaluating pretrained sentence representation models to examine their grammatical knowledge. |
| Outcome: | The proposed methods show that the model has significant knowledge of the licensing environment but its success varies widely across different methods. |
SQuALITY: Building a Long-Document Summarization Dataset the Hard Way (2022.emnlp-main)
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| Challenge: | Existing summarization datasets often have issues that seriously limit their usability. |
| Approach: | They propose a faster but more straightforward approach to developing summarization benchmark data . they use a protocol that hires highly-qualified contractors to read stories and write original summaries from scratch . |
| Outcome: | The proposed protocol is faster but more straightforward than scraping summaries from everyday text. |
jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models (2020.acl-demos)
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Yada Pruksachatkun, Phil Yeres, Haokun Liu, Jason Phang, Phu Mon Htut, Alex Wang, Ian Tenney, Samuel R. Bowman
| Challenge: | jiant is an open source toolkit for conducting multitask and transfer learning experiments on English NLU tasks. |
| Approach: | They introduce jiant, an open source toolkit for conducting multitask and transfer learning experiments on English NLU tasks. |
| Outcome: | The proposed toolkit reproduces published performance on GLUE and SuperGLUE tasks. |
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation (2020.emnlp-main)
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| Challenge: | Existing adversarial text generation approaches can lead to generation lacking diversity or fluency, whereas perturbing in the intermediate representation space can lead a model to generate generations that are not related to the input. |
| Approach: | They propose to generate adversarial texts through controllable attributes that are known to be invariant to task labels. |
| Outcome: | The proposed model generates more diverse and fluent adversarial examples, compared to existing approaches, and is more robust against model re-training and different model architectures. |
Learning to Prioritize: Precision-Driven Sentence Filtering for Long Text Summarization (2022.lrec-1)
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| Challenge: | Neural text summarization models are limited by their maximum input length, posing a challenge to summarizing longer texts comprehensively. |
| Approach: | They propose a pre-processing layer that removes low-quality sentences in articles to improve existing summarization models. |
| Outcome: | The proposed approach improves state-of-the-art summarization models on WikiHow and Reddit TIFU datasets by 3.84 and 8.57 points on the full test set and the long article subset. |
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022.emnlp-demos)
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Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina Mcmillan-major, Anna Shvets, Ashish Upadhyay, Bernd Bohnet, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez Beltrachini, Leonardo F . R. Ribeiro, Lewis Tunstall, Li Zhang, Mahim Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou
| Challenge: | Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work. |
| Approach: | They propose to use the Generation, Evaluation, and Metrics Benchmark to integrate new evaluation methods into existing evaluations. |
| Outcome: | The proposed evaluation infrastructure bridges the gap between the advantages of leaderboards and in-depth and evolving evaluations by allowing model developers to benefit from each other's work. |
Mitigating Covertly Unsafe Text within Natural Language Systems (2022.findings-emnlp)
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Alex Mei, Anisha Kabir, Sharon Levy, Melanie Subbiah, Emily Allaway, John Judge, Desmond Patton, Bruce Bimber, Kathleen McKeown, William Yang Wang
| Challenge: | Existing studies on text safety have focused on overtly unsafe, covertly, or indirectly unsafe statements. |
| Approach: | They propose a method to identify physical harm-causing statements as overtly, covertly or indirectly unsafe and a solution to mitigate the generation of such statements. |
| Outcome: | The proposed methods identify the type of unsafe language that can cause physical harm and identify mitigation strategies to inspire future researchers to tackle this challenging problem. |
Can We Improve Model Robustness through Secondary Attribute Counterfactuals? (2021.emnlp-main)
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| Challenge: | Recent research has explored how models rely on spurious correlations and how counterfactual data augmentation (CDA) can mitigate such issues. |
| Approach: | They propose a context-aware methodology which takes into account the impact of secondary attributes on the model’s predictions and increases sensitivity for secondary attributes over reweighted counterfactually augmented data. |
| Outcome: | The proposed approach improves sliced accuracy on the original dataset by 7% compared to existing methods and provides guidelines to extend this to other tasks. |
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling (P19-1)
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Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen, Benjamin Van Durme, Edouard Grave, Ellie Pavlick, Samuel R. Bowman
| Challenge: | State-of-the-art models in natural language processing (NLP) often incorporate sentence encoder functions which generate a sequence of vectors intended to represent the in-context meaning of each word in an input text. |
| Approach: | They conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks as alternatives and complements to language modeling. |
| Outcome: | The proposed model can be used to train sentences on language modeling tasks. |
Foveate, Attribute, and Rationalize: Towards Physically Safe and Trustworthy AI (2023.findings-acl)
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| Challenge: | Covertly unsafe text is an area of particular interest as it is difficult to detect as harmful . previous work focused on explicit violent text and typically expressed through violent keywords. |
| Approach: | They propose a framework that leverages external knowledge for trustworthy rationale generation in the context of safety. |
| Outcome: | The proposed framework improves safety classification accuracy by 5.9% on the SafeText dataset, and shows that it is more accurate than previous frameworks. |
SATBench: Benchmarking LLMs’ Logical Reasoning via Automated Puzzle Generation from SAT Formulas (2025.emnlp-main)
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Anjiang Wei, Yuheng Wu, Yingjia Wan, Tarun Suresh, Huanmi Tan, Zhanke Zhou, Sanmi Koyejo, Ke Wang, Alex Aiken
| Challenge: | SATBench is a benchmark for evaluating the logical reasoning capabilities of large language models (LLMs) through logical puzzles derived from Boolean satisfiability (SAT) problems. |
| Approach: | They propose a benchmark to evaluate logical reasoning capabilities of large language models (LLMs) using logical puzzles derived from Boolean satisfiability problems. |
| Outcome: | The proposed model achieves 65.0% accuracy on hard UNSAT problems, close to the baseline of 50%. |
Check Your Work: Structured Checklist Feedback for Improving Large Language Models (2026.acl-long)
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| Challenge: | Recent advances in Large Language Models have been driven by verifiable feedback in deterministic domains like mathematics and code. |
| Approach: | They propose to decompose granular, prompt-specific checklists into a scalar reward and use them to transform them into skalar rewards. |
| Outcome: | The proposed approach yields an 11.8% win-rate improvement on AlpacaEval 2.0 using Qwen3-8B, outperforming holistic reward models and existing checklist baselines. |
Asking and Answering Questions to Evaluate the Factual Consistency of Summaries (2020.acl-main)
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| Challenge: | Existing automatic evaluation metrics for summarization are insensitive to factual inconsistencies. |
| Approach: | They propose an automatic evaluation protocol that detects factual inconsistencies in a model-generated summary. |
| Outcome: | QAGS has higher correlations with human judgments of factual consistency than other evaluation metrics. |
DOCMASTER: A Unified Platform for Annotation, Training, & Inference in Document Question-Answering (2024.naacl-demo)
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| Challenge: | DOCMASTER is a platform for annotating PDF documents, model training, and inference, tailored to document question-answering. |
| Approach: | They propose to integrate layout information into a unified platform for annotating PDF documents, model training, and inference tailored to document question-answering. |
| Outcome: | The proposed platform is designed for annotating PDF documents, model training, and inference, tailored to document question-answering. |
Improving Classifier Robustness through Active Generative Counterfactual Data Augmentation (2023.findings-emnlp)
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| Challenge: | Existing methods for finding meaningful counterfactuals rely on human annotation or implicit label invariance . a small amount of human-annotated counterf actual data can generate a robust dataset with learned labels. |
| Approach: | They propose a framework that generates counterfactuals by actively sampling from regions of uncertainty and automatically labeling them with a learned auxiliary classifier. |
| Outcome: | The proposed framework generates a large number of diverse counterfactuals and labels them with a learned classifier. |
FactCG: Enhancing Fact Checkers with Graph-Based Multi-Hop Data (2025.naacl-long)
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| Challenge: | Prior research on training grounded factuality classification models to detect hallucinations in large language models (LLMs) has relied on public natural language inference (NLI) data and synthetic data. |
| Approach: | They propose a method that leverages multi-hop reasoning on context graphs extracted from documents to generate complex multi-level claims without relying on LLMs to decide data labels. |
| Outcome: | The proposed model outperforms GPT-4-o on the LLM-Aggrefact benchmark with much smaller model size. |
EquiBench: Benchmarking Large Language Models’ Reasoning about Program Semantics via Equivalence Checking (2025.emnlp-main)
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Anjiang Wei, Jiannan Cao, Ran Li, Hongyu Chen, Yuhui Zhang, Ziheng Wang, Yuan Liu, Thiago S. F. X. Teixeira, Diyi Yang, Ke Wang, Alex Aiken
| Challenge: | EquiBench is a new benchmark to evaluate large language models' ability to reason about program semantics . Unlike natural language, code is executable. |
| Approach: | They propose a benchmark to evaluate large language models through equivalence checking . EquiBench consists of 2400 program pairs across four languages and six categories . |
| Outcome: | The proposed benchmark consists of 2400 program pairs across four languages and six categories. |